A Review of Soil Temperature Estimation Research Based on Machine Learning

Abstract

Soil temperature is an important physical quantity in earth science research.In the study of Land-atmosphere interaction， soil temperature not only affects the physical， biological and chemical processes of the underlying surfaces， but also plays an important role in the energy and material exchange between land and atmosphere.With the availability of more and more relevant data， machine learning methods have been introduced into soil temperature prediction by more and more researchers， and have surpassed the performance of statistical models and physical models in many tasks.This paper compares three common methods of soil temperature calculation： statistical model， physical model and machine learning method， and briefly introduces the principles and characteristics of various machine learning models applied to soil temperature research.Based on domestic and foreign literatures， the research progress of traditional machine learning and deep learning in three aspects of soil temperature is summarized.In the study of the spatial distribution of soil temperature， traditional machine learning methods can learn spatial characteristics through the spatial heterogeneity of influencing factors， and use site observation data to calculate the temperature in the depth of the soil， but the model effect weakens as the soil depth increases.While deep learning model has a structure that can extract spatial features， it has high requirements on the amount of data， and is only used for remote sensing inversion of surface temperature in current research.In the study of soil temperature time series， the traditional machine learning method with periodic information has better performance， the sequence learning model in deep learning can automatically capture the law of soil temperature changes， and the hybrid model combined with the non-stationary sequence analysis method can fully consider the continuity and periodicity of soil temperature changes.Due to the complexity of land surface processes， there are few studies on the temporal and spatial variation of soil temperature.Based on model characteristics and research status， this paper summarizes the applicability of machine learning in soil temperature prediction， and discusses data selection， model selection， and model evaluation methods.Different data conditions and research purposes determine the choice of data and models.Decision Tree methods can provide a certain degree of interpretability through visualization.Support Vector Machines can be applied to situations with a small amount of data， and Extreme Learning Machines can meet the needs of fast computing.Due to the lack of physical constraints in machine learning， model testing and comparison of results should be emphasized when applied to soil temperature prediction.In view of the challenges of current research， the future work is prospected.The use of machine learning methods to predict soil temperature can be carried out from three aspects in the future： integrating prior scientific knowledge into the learning model， combining remote sensing data and multi-layer observations for soil temperature three-dimensional spatial modeling， and using convolutional recurrent neural networks for spatio-temporal modeling.

Keywords

• soil temprature
• machine learning
• time series